AI/ML
LLM-Powered Research Assistant
Created a research assistant capable of reading and reasoning over academic papers using LangChain, Ollama, Qdrant, and Elasticsearch.
Senior Software Engineer
2024
completed
Project Overview
Created a research assistant capable of reading and reasoning over academic papers using LangChain, Ollama, Qdrant, and Elasticsearch.
Implemented ingestion pipelines for paper datasets and vector search APIs for contextual retrieval.
Enabled users to query, summarize, and compare research insights through natural language.
Challenges & Solutions
Challenges
- Handling large-scale PDF datasets efficiently
- Balancing semantic and keyword-based search precision
- Running local LLMs with low-latency responses
Solutions
- Used Qdrant for high-performance vector storage and similarity search
- Integrated Elasticsearch for full-text indexing and hybrid retrieval
- Optimized Ollama inference pipelines for responsive local reasoning
Results & Impact
Research Efficiency
Enabled users to find and summarize relevant papers up to 10x faster
System Architecture
Deployed modular RAG system supporting scalable dataset ingestion
Practical Utility
Used by peers to prepare literature reviews and research comparisons
Technologies Used
PythonLangChainQdrantOllamaElasticsearch
Key Metrics
performanceImproved retrieval relevance by combining vector and keyword search
impactEnhanced academic research productivity
Project Details
CategoryAI/ML
Year2024
Statuscompleted
RoleSenior Software Engineer